# Generative Models: An Interdisciplinary Perspective

arXiv: https://arxiv.org/abs/2208.06011 · HTML: https://mlsanity.com/paper/2208.06011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [On the Properties and Estimation of Pointwise Mutual Information Profiles](https://mlsanity.com/paper/2310.10240/) (2023)
- [Data Science Principles for Interpretable and Explainable AI](https://mlsanity.com/paper/2405.10552/) (2024)

## Abstract (arXiv)

By linking conceptual theories with observed data, generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular biology, theory building in particle physics, and resource allocation in epidemiology, for example. We introduce the probabilistic and computational concepts underlying modern generative models and then analyze how they can be used to inform experimental design, iterative model refinement, goodness-of-fit evaluation, and agent-based simulation. We emphasize a modular view of generative mechanisms and discuss how they can be flexibly recombined in new problem contexts. We provide practical illustrations throughout, and code for reproducing all examples is available at https://github.com/krisrs1128/generative_review. Finally, we observe how research in generative models is currently split across several islands of activity, and we highlight opportunities lying at disciplinary intersections.